Multi-player Multi-issue Negotiation with Complete Information

Abstract

This paper presents a multi-player multi-issue negotiation model to solve a resource allocation problem. We design a multilateral negotiation protocol, by which rational players bid sequentially in consecutive rounds till a deadline. Every player's bid is a combination of all resource allocations for himself. In this framework, we perform a thorough theoretical analysis of the negotiation with complete information, which is a preliminary for the more complex incomplete information case. We show that, under a complete information setting, we can derive the negotiation strategies that form a subgame perfect equilibrium outcome. We also show that when a discount factor exists, an agreement will be reached immediately at the end of the first negotiation round. By making trade-offs between issues, the utility that every player gets in the equilibrium outcome is maximized and the solution is Pareto optimal.

Efficient Methods for Multi-agent Multi-issue Negotiation: Allocating Resources

“…Its two-player single-issue negotiation model has been extended to the multi-player negotiation (e.g., [2,10]) or the multi-issue negotiation (e.g., [1,11]). The multi-player multi-issue negotiation has been studied in [8,12]. A major limitation of the game-theoretic methods is that the equilibrium solutions are difficult to apply in practice, especially in the negotiation with incomplete information or non-linear utility functions or both.…”

“…A major limitation of the game-theoretic methods is that the equilibrium solutions are difficult to apply in practice, especially in the negotiation with incomplete information or non-linear utility functions or both. In this work, we adopt the multi-player multi-issue negotiation model proposed in [8], but propose heuristic computational methods of negotiation strategies of agents with incomplete information and non-linear utility functions.…”

“…However, if an agent needs to propose partitions for all in the negotiation between more than two agents, he has to decide how his opponents divide the issues left by him. Considering that every agent actually only focuses on his own parts and does not know the preferences of his opponents, we let agents bid in the way that every agent only proposes his own desired parts of issues [8]. We let…”

“…These two points make each agent difficult to determine his counter-proposal based on two proposals of his opponents directly. To solve the problem, first, we let each agent make bids only for his own parts of two issues [8]. Second, we introduce the reference point to model the bidding of the other two agents to guide the current bidder's decision.…”

Distributed consensus‐based routing protocol with multiple mobile sinks support for wireless sensor network

“…Among the existing works involving multiple sinks are those of Basagni et al [2], Choi et al [4,[13][14][15]. Since there is no fixed infrastructure or cooperative control base in distributed wireless sensor networks, the data transmission requires a mutual aid among the nodes, through consensus [16][17][18][19][20], negotiation-based methods [21][22][23], learning-based approach [24,25] or scheduled-based mechanisms [26,27]. However, these mechanisms have some similarities to the centralised systems which incur a high computational cost and may be quickly exhausted.…”